ForestSphere

The ForestSphere project aims to develop a Digital Twin (DT) for forest management, recognizing their socio-economic and ecological importance.Forests contribute significantly to the EU's GDP and serve as critical carbon sinks. However, wildfires pose a substantial threat to forests, leading to environmental damage, loss of life, and economic losses.

Despite advancements in DT technology in various sectors, current literature has limited exploration of its application in forest management, particularly regarding wildfire prediction.

The ForestSphere project intends to bridge this research gap by creating a Forest DT that combines multimodal and multi-scale data from various sensors to enhance forest health monitoring and wildfire risk assessment. The project's key objectives include:

• Developing an open, scalable, and modular Digital Twin architecture.
• Enabling precise, high-resolution forest mapping using advanced sensing techniques.
• Implementing data processing for dynamic updates of the DT.
• Utilizing AI for predictive wildfire analytics.
• Designing user-centric tools for decision support to assist firefighting teams and policymakers.
• Conducting pilot cases to demonstrate the Forest DT's applications in forest and wildfire management.Challenges include managing high computation costs, ensuring real-time data synchronization, and effectively integrating diverse data sources.

The project follows a structured three-phase methodology: Build, Optimize and Deploy, and the project promises significant socio-economic benefits by providing real-time data to authorities, aiding infrastructure management, and supporting civil protection efforts during wildfires.